The Rise of Agentic AI: Transforming Workflows in C# Development

Hey there! If you've been keeping an eye on AI developments lately, you've probably noticed something exciting happening—agentic AI is changing how we build applications, especially in the .NET ecosystem. I know it can feel overwhelming when new paradigms emerge (trust me, I've been there), so let's walk through this together and figure out what agentic AI means for us as C# developers.

What's This Agentic AI Thing Anyway?

When I first heard about "agentic AI," I thought it was just another buzzword. But after diving in, I realized it represents a fundamental shift in how we build AI-powered applications. Instead of simple request-response patterns, we're talking about AI systems that can plan, use tools, make decisions, and complete complex multi-step tasks autonomously.

According to recent industry analysis, agentic AI systems are expected to grow from just 1% of business applications in 2024 to 30% by 2028. That's a massive shift, and it's happening fast.

Key Terms to Know:

• Agent: An AI entity that can reason, plan, and execute tasks using available tools
• Orchestration: The coordination of multiple agents or workflow steps to accomplish complex goals
• Tool Calling: The ability for AI models to invoke external functions and APIs
• Multi-Agent Systems: Multiple specialized agents working together, each handling specific responsibilities

Why C# Developers Should Pay Attention

Let me be honest—when I started exploring agentic AI frameworks, I was confused by the sheer number of options. Microsoft's ecosystem includes Semantic Kernel and AutoGen, which are powerful but can feel complex when you're just trying to build something practical.

The good news? The .NET ecosystem now offers several approaches to building agentic workflows, each with different strengths. Let's explore what makes agentic AI powerful and how to get started without getting overwhelmed.

Installing What You Need

Before we dive into code, let's get set up. I find it helpful to start with a solid foundation:

dotnet add package LlmTornado
dotnet add package LlmTornado.Agents

These packages give us everything we need to build sophisticated AI agents with minimal boilerplate. You can explore more examples in the LlmTornado repository.

Building Your First AI Agent

Let's start simple. Here's how to create a basic agent that can actually help you:

using LlmTornado;
using LlmTornado.Agents;
using LlmTornado.Chat;
using LlmTornado.Chat.Models;

// Initialize the API client
var api = new TornadoApi(
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY"),
    provider: LLmProviders.OpenAi
);

// Create an agent with specific instructions
var agent = new TornadoAgent(
    client: api,
    model: ChatModel.OpenAi.Gpt41.V41Mini,
    name: "ResearchAssistant",
    instructions: "You are a helpful research assistant that provides detailed, well-structured answers.",
    streaming: true
);

// Run the agent and get results
Conversation result = await agent.Run("What are the benefits of using AI agents in software development?");

Console.WriteLine(result.Messages.Last().Content);

This might look straightforward, but here's what's powerful: the agent maintains conversation context, can be configured with different behaviors, and supports streaming responses for better user experience.

Adding Tools: Where the Magic Happens

When I first learned about tool calling, it clicked—this is how we bridge AI reasoning with real-world actions. Let's create an agent that can actually fetch information:

using System.ComponentModel;
using LlmTornado.ChatFunctions;

// Define a weather tool
[Description("Get the current weather for a location")]
public static string GetCurrentWeather(
    [Description("The city and state, e.g. Boston, MA")] string location,
    [Description("Temperature unit: Celsius or Fahrenheit")] string unit = "Celsius")
{
    // In a real app, call a weather API here
    return $"The weather in {location} is 22°{unit[0]} and partly cloudy.";
}

// Create an agent with tools
var agent = new TornadoAgent(
    client: api,
    model: ChatModel.OpenAi.Gpt41.V41Mini,
    name: "WeatherAssistant",
    instructions: "You help users check weather conditions. Always use the GetCurrentWeather tool.",
    tools: new List<Delegate> { GetCurrentWeather }
);

// The agent will automatically call the tool when needed
Conversation result = await agent.Run("What's the weather like in Boston?");

Console.WriteLine(result.Messages.Last().Content);

The SDK automatically converts your C# methods into tools the AI can invoke. Notice how we use Description attributes—these help the model understand when and how to use each tool. This pattern is incredibly flexible because you can add any function as a tool.

Multi-Agent Orchestration: The Next Level

Here's where things get really interesting. What if you need multiple specialized agents working together? Maybe one agent does research, another analyzes data, and a third generates reports. This is where orchestration comes in.

Let me show you a practical example—a research assistant that plans searches, gathers information, and writes reports:

using LlmTornado.Agents.ChatRuntime;
using LlmTornado.Agents.ChatRuntime.RuntimeConfigurations;
using LlmTornado.Agents.DataModels;
using LlmTornado.Responses;

// Define the workflow structure
public class ResearchWorkflow : OrchestrationRuntimeConfiguration
{
    private TornadoAgent plannerAgent;
    private TornadoAgent researcherAgent;
    private TornadoAgent reporterAgent;

    public ResearchWorkflow(TornadoApi client)
    {
        // Create specialized agents
        plannerAgent = new TornadoAgent(
            client: client,
            model: ChatModel.OpenAi.Gpt5.V5Mini,
            name: "Planner",
            instructions: "Generate a list of search queries to research a topic thoroughly."
        );

        researcherAgent = new TornadoAgent(
            client: client,
            model: ChatModel.OpenAi.Gpt5.V5Mini,
            name: "Researcher",
            instructions: "Search the web and summarize findings concisely."
        );

        // Enable web search for the researcher
        researcherAgent.ResponseOptions = new ResponseRequest() 
        { 
            Tools = new[] { new ResponseWebSearchTool() } 
        };

        reporterAgent = new TornadoAgent(
            client: client,
            model: ChatModel.OpenAi.Gpt5.V5,
            name: "Reporter",
            instructions: "Synthesize research into a comprehensive report with clear structure.",
            streaming: true
        );
    }
}

// Use the workflow
var workflow = new ResearchWorkflow(api);
var runtime = new ChatRuntime(workflow);

// Handle streaming output
workflow.OnRuntimeEvent = async (evt) =>
{
    if (evt is ChatRuntimeAgentRunnerEvents runnerEvt)
    {
        if (runnerEvt.AgentRunnerEvent is AgentRunnerStreamingEvent streamEvt)
        {
            if (streamEvt.ModelStreamingEvent is ModelStreamingOutputTextDeltaEvent deltaTextEvent)
            {
                Console.Write(deltaTextEvent.DeltaText);
            }
        }
    }
};

ChatMessage report = await runtime.InvokeAsync(
    new ChatMessage(ChatMessageRoles.User, "Write a report about AI agents in enterprise software")
);

This orchestration pattern lets each agent focus on what it does best. The planner strategizes, the researcher gathers data, and the reporter synthesizes everything. Each step flows naturally into the next.

Handling Streaming and Real-Time Responses

One thing that frustrated me initially was dealing with streaming responses. Users expect to see output as it's generated, not after waiting 30 seconds. Here's how to handle streaming elegantly:

var agent = new TornadoAgent(
    client: api,
    model: ChatModel.OpenAi.Gpt41.V41Mini,
    instructions: "You are a helpful coding assistant.",
    streaming: true
);

// Define a streaming event handler
async ValueTask StreamingHandler(AgentRunnerEvents runEvent)
{
    if (runEvent is AgentRunnerStreamingEvent streamingEvent)
    {
        if (streamingEvent.ModelStreamingEvent is ModelStreamingOutputTextDeltaEvent deltaEvent)
        {
            // Write each chunk as it arrives
            Console.Write(deltaEvent.DeltaText);
        }
    }
}

// Run with streaming enabled
Conversation result = await agent.Run(
    input: "Explain how async/await works in C#",
    streaming: true,
    onAgentRunnerEvent: StreamingHandler
);

Streaming makes your applications feel responsive and professional. Users see progress immediately rather than staring at a loading spinner.

Real-World Challenges (Let's Be Honest)

I'd be doing you a disservice if I didn't mention the challenges. Research indicates that many agentic AI projects struggle with:

1. Orchestration Complexity

When you have multiple agents, coordination becomes tricky. Which agent runs when? How do you handle failures? How do you pass data between agents? The orchestration runtime pattern we explored helps, but you still need to think through your workflow carefully.

2. Data Integration

AI agents are only as good as the data they can access. You'll need to think about:

• How agents authenticate with your systems
• Rate limiting and API quotas
• Handling sensitive data securely
• Caching to reduce costs

3. Cost Management

Agentic workflows can make many API calls. Each tool invocation, each agent interaction—it adds up. Monitor your usage and implement safeguards like maximum turn limits.

4. Testing and Debugging

AI behavior can be non-deterministic. What worked yesterday might behave differently today. Build in logging, record agent interactions, and implement guardrails.

Practical Tips for Success

After building several agentic systems, here's what I've learned works:

Start Simple: Build a single-agent system first. Get comfortable with tool calling and conversation management before adding orchestration.

Use Structured Outputs: When you need reliable data extraction, use typed schemas:

public struct AnalysisResult
{
    public string Summary { get; set; }
    public string[] KeyFindings { get; set; }
    public int ConfidenceScore { get; set; }
}

var agent = new TornadoAgent(
    client: api,
    model: ChatModel.OpenAi.Gpt41.V41Mini,
    instructions: "Analyze the provided data",
    outputSchema: typeof(AnalysisResult)
);

Implement Guardrails: Validate inputs before processing and outputs before returning them to users.

Monitor Everything: Log agent decisions, tool calls, and costs. You'll thank yourself later when debugging.

The Path Forward

The growing ecosystem of C# AI tools means we have real choices now. Whether you use Semantic Kernel, AutoGen, or a provider-agnostic SDK like LlmTornado, the key is understanding the patterns: agents, tools, orchestration, and streaming.

I've found that starting with one SDK and learning it deeply is better than trying to use everything at once. LlmTornado has helped me build agents quickly because it handles the plumbing—connecting to multiple AI providers, managing conversations, converting functions to tools—while letting me focus on workflow logic.

Your Next Steps

If you're ready to experiment:

1. Build a simple agent with one or two tools
2. Add conversation memory to make it stateful
3. Try orchestrating multiple agents for a complex task
4. Implement streaming for better user experience
5. Monitor and optimize based on real usage

The future of software development includes AI agents as first-class components. Getting comfortable with agentic patterns now will serve you well as these systems become standard in enterprise applications.

Remember, everyone starts confused. The key is to build something, break it, fix it, and learn. That's how we all figure this out together.

What are you going to build first? I'd love to hear about your experiments with agentic AI in C#!